Coastline extraction techniques from multispectral satellite images are of great interest for protection and monitoring of coastal areas. In this regard, the Sentinel-2 satellites can give a great contribution thanks to their wide coverage of the earth’s surface. These images can be processed by GIS software, so as to detect the sea from all the rest. However, the traditional supervised classification requires the involvement of the operator to create suitable training sites: this approach, in addition to being associated to the operator’s skill, often takes a long time to be completed. This contribution presents a study carried out on Sentinel-2 dataset and proposes the application of an unsupervised classification method, the k-means, on four different classification indices. The coastlines extracted by unsupervised classification are therefore compared with the coastline manually vectorized from the RGB composition. The results demonstrate the effectiveness of k-means for distinguishing, in the images produced by the indices application, two clusters (water /no-water) in a reduced time lapse if compared with the traditional supervised techniques.

Orthometric heights, useful for many engineering and geoscience applications, can be obtained by GPS (Global Positioning System) surveys only when an accurate geoid undulation model (that supplies the vertical separation between the geoid and WGS84 ellipsoid) is available for the considered topic area. Global geoid height models (i.e., EGM2008), deriving from satellite gravity measurements suitably integrated with other data are free available on web, but their accuracy is often not sufficient for the user’s purposes. More accurate local models can nevertheless be acquired, but often only for a fee. GPS/levelling surveys are suitable for determining a local, accurate geoid model, but may be too expensive. This paper aims to demonstrate that GNSS (Global Navigation Satellite System) Permanent Station documents (monographs), freely available on the web and supplying orthometric and ellipsoidal heights, permit to calculate precise geoidal undulations useful to perform global geoid modelling on a local area. In fact, in this study 25 GNSS Permanent Stations (GNSS PS), located in North-Western Italy are considered: the differences between GNSS PS geoidal heights and the corresponding EGM2008 1′ × 1′ ones are used as a starting dataset for Ordinary Kriging applications. The resulting model is summed to the EGM2008 1′ × 1′, obtaining a better-performed model of the interest area. The accuracy tests demonstrate that the resulting model is better than EGM2008 grids to produce contours from a GPS dataset for large-scale mapping.

The eruption of the Hunga Tonga - Hunga Ha'apai volcano, which began on January 14,2022, is so far the largest eruption of the twenty-first century. The strong explosion it generated was able to change the physiognomy of the island, leaving only two islets. Thanks to Sentinel-2 satellite mission, it was possible to acquire images showing the island before and after the eruption. The aim of this work is to detect the changes that occurred on the island by comparing Sentinel-2 images acquired on different data (before and after the eruption). The work is developed over four basic phases: the first concerns the application of different indices to delineate the territorial boundaries of the island; next the Maximum Likelihood Classification (MLC) is applied to the indices to distinguish water and no-water classes; then the post-classification change detection technique is applied to measure the areas and the amount of soil sunk under water as a result of the eruption; finally, tests are carried out to evaluate the accuracy of the resulting coastlines as a marker of classification accuracy. The results show that the only effective index for determining the two aforementioned classes is the Modified Normalized Difference Water Index (MNDWI). That is due to the presence of lava soil which limits the effectiveness of the other indices available in the literature and normally adopted for the extraction of the coastline, e.g. the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI).